Student climate change education: The role of scientific technologies in improving public geoscience understandings
Bibliographic record
Abstract
In Canada and the United States, segments of the public misunderstand the physical science of anthropogenic global climate change (AGCC) and its connection to human society. Individuals have been shown to filter their scientific understandings through identification with specific worldviews, ecological paradigms, geographic identities or political leanings. To overcome this problem, prominent scientists and the Next Generation Science Standards (NGSS) have called for curricula and instructional approaches that emphasize learning about climate research using climate models. Using the techniques of educational research, this study presents unique empirical findings on how geoscientists can employ innovative instructional approaches and science education technologies to overcome sociocultural barriers and improve public understanding of AGCC.The chapters of this dissertation present detailed analysis and statistically significant results on the educational impact of students learning to run a National Aeronautics and Space Administration (NASA) global climate model (GCM). Through a series of case studies, this study explored how a key technology of climate science—a GCM—impacted student learning compared to ubiquitous simple climate education technologies. The central hypothesis was that student use of authentic climate science research methods and technologies will improve AGCC understanding. This study utilized a pre/post, control/treatment experimental design that allowed for comparison between instructional strategies and climate education technologies used by two groups of students. To operationalize this work, it employed research instruments such as pre/post diagnostic exams, performance-based assessments, pre/post questionnaires and 536-minutes of classroom video recordings. It also utilized quantitative statistical analysis to determine significant differences and establish what educational and sociocultural factors impacted individual student learning gains across the whole sample. Findings from this work have shown that more students succeed at understanding AGCC when exposed to inquiry research processes using scientific technologies such as GCMs. In contrast, those who learned about GCMs through lecture only showed improvement in their recall of facts tested by multiple-choice questions. Individual students' ecological paradigms and relationships to natural places also best predicted engagement (represented by class attendance) with course materials and larger learning gains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".